ID 原文 译文
57458 电子战和电磁频谱作战在现代战争中具有威慑作用，是决定战争胜负的首要因素，这在一系列局部战争和冲突中得到了充分证实. Electronic warfare and electromagnetic spectrum warfare play a deterrent role in modern war- fare，which is the primary factor determining the victory or failure of a war. It has been fully confirmed in the recent series of local wars and conflicts.
57459 冷战结束以来，美国和俄罗斯在该领域展开了激烈的竞争和对抗，新型电子战武器层出不穷. Since the end of the cold war，the United States and Ｒussia have launched fierce competition and confrontation in this field，and new types of electronic warfare weapons emerge in endlessly.
57460 梳理了近年来美俄两国在电子战武器发展方面的现状和趋势，以作为我国电子战武器研究的借鉴. It is summarized that current situation and trend of the development of elec- tronic warfare weapons in the two countries in recent years，which can be used as a reference for the de- velopment of electronic warfare weapons in China.
57461 针对未来网络转发信息库( FIB) 中多模态数据带来的差异化快速索引、高效存储转发信息和多模态数据最长前缀匹配等问题，设计了一种支持多模态数据索引的混合型 FIB，称为 Hybrid-FIB. In order to solve the problems of rapid indexing，efficient storage of forwarding information and longest prefix matching brought by multi-modal data in the forwarding information base( FIB) in the future network，a hybrid FIB based on neural networks，called Hybrid-FIB，which supports multi-modal data indexing is designed.
57462 通过对不同类型的数据进行差异化处理，得到可供神经网络模型学习的输入向量，进而训练出能够实现均匀分布的神经网络混合索引模型. Hybrid-FIB differentiates different type of data to obtain input vectors for neural network model，and then trains a neural network hybrid index model that can achieve uniform distribu- tion.
57463 为了实现多模态数据最长前缀匹配，在片上静态随机存取存储器中部署两组 Hybrid-FIB 结构.实验结果表明，该混合型 FIB 在误判率、存储消耗及吞吐量等方面具备优异性能. Experiments show that deploying two sets of Hybrid-FIB on the static random access memory can not only achieve the longest prefix matching of the multi-modal data，but also have better retrieval speed and misjudgment rate than the current network.
57464 WiFi 信道状态信息( CSI) 被广泛应用于被动式( 非侵入式) 人体行为判断，为使用现有商用设备实现人体连续动作计数与识别，提出了一种 Wi-ACＲ 方法. Nowadays WiFi channel state information is widely applied in passive ( unobtrusive) human continuous activity recognition. The article uses commercial off-the-shelf devices and proposes a human action counting and recognition ( Wi-ACＲ) method，based on channel state information( CSI) .
57465 先利用阈值和活动指标检测出一组连续动作发生的区间和时间，再通过 peak-find 算法统计出动作的数量，并确定每个动作的开始和结束时间; Wi-ACＲ takes advantage of the threshold algorithm and action indicator to detect the start and end time of a set of continuous actions，and then counts the number of actions through the peak-find algorithm and determines the start and end time of each action.
57466 再分别采用基于波形特征的动作识别模型和基于统计特征的动作识别模型，得到动作识别结果. After that，Wi-ACＲ takes the waveform-feature-based action recog- nition model and the statistical-feature-based action recognition model to obtain action recognition results respectively.
57467 实验评估结果表明，Wi-ACＲ 对动作计数的准确率可达95% ，两类识别模型对于 2 个动作( 深蹲和走) 的平均识别精准率为 90% . Experiments show that Wi-ACＲ can achieve action counting accuracy of 95% and recogni- tion accuracy of 90% with these two recognition models，in the scenarios with two types of actions( i. e. squat and walk) occurring simultaneously.